Due to hardware customization capabilities, FPGA-based graph processing accelerators achieve significantly higher energy efficiency than many general-purpose computing engines. However, designing these accelerators remains a substantial challenge for high-level users. To overcome the programming barrier, FPGA-based accelerator design frameworks on top of generic graph processing programming models have been developed to automate accelerator generation through pre-built templates. However, they often tightly couple graph processing algorithms, programming models and processing paradigms, and accelerator architectures, which severely limits the expression scope of the algorithms and may also restrict the performance when the generated accelerators fail to suit dynamic processing patterns of the graph processing algorithms.

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency.

With the widespread adoption of the Internet of Things (IoT) and various smart medical devices, the volume of medical data has dramatically increased, making the processing of medical Internet of Things (IoMT) data increasingly challenging. Due to the integration of edge computing and cloud computing, IoMT can allocate increased computing and storage resources in proximity to the terminal, addressing the low-latency requirements of computationally intensive tasks. While existing initiatives have shifted services to edge servers, they have not taken into account the joint impact of task priorities and mobile computing services on Mobile Edge Computing (MEC) networks. Fortunately, the rapidly advancing field of Artificial Intelligence (AI) has proven effective in some resource allocation applications in recent years. In this article, we propose a mobile edge computing-based intelligent healthcare multitasking processing system aimed at addressing the issue of service prioritization in medical scenarios. Considering energy consumption and latency, we present a multi-objective task-aware service offloading algorithm under the framework of end-edge-cloud collaborative IoMT systems, employing deep deterministic policy gradients (DDPG). Adaptability to the diversity of different services is achieved through dynamic adjustments based on various business types and system requirements. Finally, the effectiveness of DDPG for IoMT is validated using real-world data.

Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.

Blockchain with its decentralization, anti-tampering, traceability, anonymity and other technical characteristics, which provides mitigations to society issues such as data governance, information silo. However, the blockchain has inherent technical shortcomings such as its data openness and transparency and data permanent storage and other characteristics, which leads to security issues cannot be ignored, and may be subject to BGP hijacking attacks, distributed denial of service attacks, and node vulnerability attacks, which will bring greater risks to some private information, so an important alternative is to apply the covert channel. In this paper, we propose a secret embedding scheme based on Bitcoin transactions pattern controlling, where miner behaves secret embedding based on each single block transaction counts under block generating process. Our scheme adopts Gray code encoding to preprocess the covert information to be transmitted to improve the robustness of the scheme, uses transaction counts as a carrier and constructs a mapping table between transaction counts and secret information, which is embedded by intentionally modulating the transaction counts in the candidate blocks. Moreover, the distribution pattern of public traffics is simulated, so that the generated covert traffics are of undetectability. The implementation of our scheme is established under the real transaction scenarios, and our performance analysis shows that the proposed scheme is stealthy via KS test and KLD test, in addition, compared to the existing blockchain covert storage channel schemes based on transaction modification, our scheme’s overhead of transaction fee is zero.

Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.

With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

Aiming at solving the clutter flooding problem in the traditional cross-correlation reverse time migration (RTM) of ground penetrating radar (GPR), we proposed an improved RTM method based on multi-traces cross-correlation window (MCW) imaging condition. The main difference between the proposed method and the traditional direct stacking is that it can effectively enhance the effective signal while weakening the clutter by performing MC calculation on the single trace imaging results, avoiding the enhancement of both clutter interference and effective signal concurrently by direct stacking. Secondly, the window threshold is set according to the GPR observation accuracy, and the effective signal in the MC result is retained as the abnormal region window, while the imaging results in the non-abnormal region are discarded, so as to suppress the clutter and retain the abnormal region information. Numerical experiments show that, compared with the traditional RTM and total variation de-noising method with cross-correlation imaging conditions, the MCW imaging condition can accurately locate abnormal region, suppress clutter interference, and have the advantage of no loss of effective information, which greatly improves the imaging quality. Finally, the proposed method is applied to the measured data to verify the practicability and effectiveness in practical engineering applications.

Reverse time migration (RTM) has the advantage of high-precision imaging, and it can converge the radar wave back to its actual position, making it widely used in radar exploration. However, there are artifacts, low-frequency noise and fuzzy deep imaging in RTM results. Researchers have proposed full wavefield separation imaging condition and total variation (TV) technique, both of which could suppress noise and artifacts. However, the original wavefield separation method was considerably limited by its extensive calculation, and it cannot solve the problem of weak energy of imaging in the deep zone; the conventional TV technique was likely to be affected by artifacts due to the inevitable over-smoothing-suppression of anomaly edges. To address these issues, this paper improves the RTM methodology by combining an optimized full wavefield separation based on Poynting vector imaging condition and TV-L1 based artifacts suppressing technique. Specifically, the physical significance of the Poynting vector is introduced to separate the wavefield for reducing the calculation burden; the compensation function is integrated with the imaging condition to compensate for the deep energy; the TV-L1 based artifacts suppressing method is used to resolve the imaging problem of loss of specific and edge details. Synthetic data and laboratory data experiments are carried out to verify the effectiveness and practicability of the proposed RTM methodology.

Multi-parameter elastic full waveform inversion (FWI) makes full use of the dynamic and kinematic information of all seismic wavefield. Through the mutual constraint and verification of the three parameters of P-wave velocity, S-wave velocity, and density, the joint evaluation is carried out, which is helpful to understand the structural and lithologic information of underground media more comprehensively. The bottleneck restricting the multi-parameter FWI is the large amount of calculation and low efficiency. To improve this problem, multiple shots are directly superimposed to form super shots. While it usually results in an unstable inversion due to that a large amount of crosstalk noise will be easily generated between adjacent shots. In this paper, we introduce the random source-encoding strategy to improve the inversion efficiency and load the total-variation (TV) regularization term to suppress the crosstalk noise, but it also brings the problem of regularization parameters selection for multi-parameter FWI. Thus, the projection method is applied to directly load the regularization term into the model as a constraint, which avoids the unsatisfactory results caused by the improper selection of regularization parameters and effectively improves the ill-posedness of inversion. Finally, three examples of the graben, the 1994BP, and the overthrust model are used to prove that the proposed algorithm based on random source-encoding and projection regularization can effectively improve the inversion efficiency, suppress noise, and has good practicability and adaptability.

Ground-penetrating radar (GPR) has been regarded as a potentially efficient way of evaluating the growth status of trees and preventing deterioration associated with trunk defects. The majority of current GPR data inversions, however, focused on imaging the macroscale location of defects. As the first attempt to seek a preferable quantitative inversion methodology for specifying tree protection and remedies, this article proposes a full-waveform inversion (FWI) approach involving dual-parameter attributes applied to common-offset GPR data from a commercial antenna. Specifically, the synchronous inversion of both dielectric constant and conductivity improves the identification accuracy of certain defect types. In particular, both a multifrequency strategy and total-variation (TV) regularization are seamlessly introduced to assure inversion stability by overcoming local minima and cycle skipping. Through an irregular trunk model test, the effectiveness of the optimized inversion is initially verified by presenting the precise features of the crack, hollow, and decay with the dual-parameter inversion results. In addition, several other synthetic trunk models and in-site trunk model tests further demonstrate the robustness and practicability of the proposed algorithm, which can offer more specific and comprehensive guidance for the formulation of tree protection and restoration measures.

Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN. In general, we show that mean and standard deviation are not always the most appropriate choice for the centering and scaling procedure within the BN transformation, particularly if ReLU follows the normalization step. We present a Generalized Batch Normalization (GBN) transformation, which can utilize a variety of alternative deviation measures for scaling and statistics for centering, choices which naturally arise from the theory of generalized deviation measures and risk theory in general. When used in conjunction with the ReLU non-linearity, the underlying risk theory suggests natural, arguably optimal choices for the deviation measure and statistic. Utilizing the suggested deviation measure and statistic, we show experimentally that training is accelerated more so than with conventional BN, often with improved error rate as well. Overall, we propose a more flexible BN transformation supported by a complimentary theoretical framework that can potentially guide design choices.

Discovering the latent topics within texts has been a fundamental task for many applications.However, conventional topic models suffer different problems in different settings.The Latent Dirichlet Allocation (LDA) may not work well for short texts due to the data sparsity (i.e., the sparse word co-occurrence patterns in short documents).The Biterm Topic Model (BTM) learns topics by modeling the word-pairs named biterms in the whole corpus.This assumption is very strong when documents are long with rich topic information and do not exhibit the transitivity of biterms.In this paper, we propose a novel way called GraphBTM to represent biterms as graphs and design Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterms.To overcome the data sparsity of LDA and the strong assumption of BTM, we sample a fixed number of documents to form a mini-corpus as a training instance.We also propose a dataset called All N ews extracted from (Thompson, 2017), in which documents are much longer than 20 Newsgroups.We present an amortized variational inference method for GraphBTM.Our method generates more coherent topics compared with previous approaches.Experiments show that the sampling strategy improves performance by a large margin.